In order to improve the feature denoising ability on the high-noise data, the PRS-block uses pixel-wise soft thresholding to boost the SBR on the latent representations. Improved upon the stable threshold generation strategy from [1]}, where the same thresholds are shared among all pixels, our strategy is to predict the pixel-wise optimal thresholds for different inputs. As shown in Fig. REF , the input is denoted by \(X \in \mathbb {R}^{B \times C \times T \times M \times N}\) , where \(B\)  is the batch size, \(C\)  is the number of channels, and \(M \times N\)  is the input resolution. First, two \(3\times 3\times 3\)  convolutional layers followed by a reshape layer are used to obtain the residual input, which is denoted by \(X^{r} \in \mathbb {R}^{BC \times T \times M \times N}\) , for the pixel-wise soft thresholding operation. Then, \(X^{r}\)  is fed into the dynamic thresholds generation block, which is composed of two parallel branches. In the first branch, a normalization layer is used at the beginning to improve the training efficiency [2]}. Then three 2D convolutional layers with \(1\times 1\)  kernel size are proposed to calculate the pixel-wise scaling parameter \(S \in \mathbb {R}^{BC \times M \times N}\) . At the end of this branch, a sigmoid function is used to confine the output within \([0,1]\) . In the second branch, we average the absolute values of \(X^{r}\)  along the time dimension. Then the pixel-wise thresholds \(\tau \in \mathbb {R}^{BC \times M \times N}\)  are calculated by
\(\tau _{b,i,j} = S_{b,i,j} \cdot \mathop {\mathrm {average}}\limits _{t}(|X^{r}_{b,t,i,j}|),\) 
